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Knit directory: 13384_GBMHGG_SPP1_Xenium/
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suppressPackageStartupMessages({
library(workflowr)
library(arrow)
library(Seurat)
library(SeuratObject)
library(SeuratDisk)
library(tidyverse)
library(tibble)
library(ggplot2)
library(ggpubr)
library(ggrepel)
library(googlesheets4)
library(workflowr)})
setwd("/home/hnatri/13384_GBMHGG_SPP1_Xenium/")
set.seed(9999)
options(scipen = 99999)
options(ggrepel.max.overlaps = Inf)
#source("/home/hnatri/13384_GBMHGG_SPP1_Xenium/code/colors_themes.R")
source("/home/hnatri/13384_GBMHGG_SPP1_Xenium/code/plot_functions.R")
# Cluster colors
main_clusters <- as.factor(c(0, seq(1:20)))
main_cluster_col <- colorRampPalette(brewer.pal(10, "Paired"))(nb.cols <- length(main_clusters))
names(main_cluster_col) <- levels(main_clusters)
# Copied to isilon /tgen_labs/banovich/PIPAC/Seurat
seurat_data <- readRDS("/tgen_labs/banovich/BCTCSF/13384_GBMHGG_Xenium/Seurat/spatial_clustered_NN30_PC50_Seurat.rds")
seurat_data <- NormalizeData(seurat_data)
VariableFeatures(seurat_data) <- rownames(seurat_data)
seurat_data <- ScaleData(seurat_data)
DimPlot(seurat_data,
reduction = "sp",
group.by = "leiden_1.0",
cols = main_cluster_col,
raster = T,
label = F) +
coord_fixed(ratio = 1) +
theme_minimal() +
NoLegend() +
ggtitle("TMA1_0069754")
gs4_deauth()
all_sheets <- gs4_get("https://docs.google.com/spreadsheets/d/1V1YxYPMI-J1ilZ3ooTDM323s5VMIf7E9f5XsuIOaGAg/edit?usp=sharing")
sheet_names(all_sheets)
[1] "Metadata" "Main cluster annotations"
[3] "Immune annotations" "Immune top markers"
[5] "Non-immune annotations" "Non-immune top markers"
[7] "Multi-tissue and cancer basepanel" "Custom probes"
multissue_cancer <- read_sheet(all_sheets, sheet = "Multi-tissue and cancer basepanel")
custom_panel <- read_sheet(all_sheets, sheet = "Custom probes")
#unique(multissue_cancer$Annotation)
fibroblast_markers <- multissue_cancer[grep("Fibroblast", multissue_cancer$Annotation),]$Gene # 65
immune_markers <- multissue_cancer[grep("Immune", multissue_cancer$Annotation),]$Gene # 6
macrophage_markers <- multissue_cancer[grep("Macrophage", multissue_cancer$Annotation),]$Gene # 70
granulocyte_markers <- multissue_cancer[grep("Granulocytes", multissue_cancer$Annotation),]$Gene # 25
dendritic_markers <- c(multissue_cancer[grep("dendritic", multissue_cancer$Annotation),]$Gene,
multissue_cancer[grep("Dendritic", multissue_cancer$Annotation),]$Gene) #22
tcell_markers <- multissue_cancer[grep("T cells", multissue_cancer$Annotation),]$Gene # 62
bcell_markers <- multissue_cancer[grep("B cells", multissue_cancer$Annotation),]$Gene # 45
proliferation_markers <- multissue_cancer[grep("Proliferation", multissue_cancer$Annotation),]$Gene # 7
endothelial_markers <- multissue_cancer[grep("Endothelial", multissue_cancer$Annotation),]$Gene # 82
all_genes <- c(custom_panel$Gene) #multissue_cancer$Gene
all_genes <- gsub("CD11b", "ITGAM", all_genes)
all_genes <- gsub("CD11c", "ITGAX", all_genes)
all_genes <- gsub("CD31", "PECAM1", all_genes)
all_genes <- gsub("CD45", "PTPRC", all_genes)
all_genes <- gsub("HLA-DR", "HLA-DRA", all_genes)
all_genes <- gsub("HLA.DPB1", "HLA-DPB1", all_genes)
all_genes <- gsub("IFNGR", "IFNGR1", all_genes)
all_genes <- gsub("TGFβ", "TGFB1", all_genes)
all_genes <- gsub("TGFβR", "TGFB1R", all_genes)
all_genes <- gsub("TGFB1R", "TGFBR1", all_genes)
all_genes <- gsub("IL-1R", "IL1R2", all_genes)
all_genes <- sort(all_genes)
#setdiff(all_genes, rownames(seurat_data))
#grep("TGFB", rownames(seurat_data), value = T)
for(gene in all_genes){
message(gene)
print(FeaturePlot(seurat_data,
slot = "data",
features = gene,
order = T,
reduction = "sp",
raster = T,
ncol = 1,
cols = c("gray89", "tomato3")) &
coord_fixed(ratio = 1) &
theme_bw() &
NoLegend())
}
ALDOC
APOE
BMPR1B
BNIP3L
C1QA
C1QB
C1QC
CAMK2B
CCL2
CCL4
CD44
CD47
CD63
CD72
CD74
CD80
CDH2
CDK4
CDKN2B
CEMIP2
COL1A1
CTSB
CX3CR1
CXCL1
CXCL12
CXCL8
ENO1
FCGR3A
FGFR1
FGFR2
FLT1
FN1
GABRD
GAPDH
GFAP
HIF1A
HK2
HLA-DPB1
HLA-DRA
HMOX1
HOMER1
ICAM1
IFI6
IFNG
IFNGR1
IGFBP2
IGFBP3
IL13RA2
IL1A
IL1B
IL1R2
ISG15
ITGA3
ITGA4
ITGA5
ITGAE
ITGAM
ITGAX
ITGB1
JAK1
LDHA
LYZ
MADCAM1
MARCO
MIF
MX1
NF1
NLRP3
NR4A3
NRP1
OLIG1
OLIG2
OPALIN
OSM
P2RY12
P2RY13
P4HB
PDK4
PECAM1
PGK1
PTPRC
PTPRD
RHOB
RNASE1
S100A8
S100A9
SLC2A1
SPP1
STAT3
STAT4
SYN1
TERT
TGFB1
TGFBI
TGFBR1
TIMP1
TMEM119
TOX
TP53
TYROBP
VCAM1
VEGFA
VEGFB
sessionInfo()
R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] ComplexHeatmap_2.18.0 viridis_0.6.3 viridisLite_0.4.2
[4] RColorBrewer_1.1-3 googlesheets4_1.1.0 ggrepel_0.9.3
[7] ggpubr_0.6.0 lubridate_1.9.2 forcats_1.0.0
[10] stringr_1.5.0 dplyr_1.1.2 purrr_1.0.1
[13] readr_2.1.4 tidyr_1.3.0 tibble_3.2.1
[16] ggplot2_3.4.2 tidyverse_2.0.0 SeuratDisk_0.0.0.9021
[19] Seurat_5.0.1 SeuratObject_5.0.1 sp_1.6-1
[22] arrow_21.0.0.1 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.20 splines_4.3.0 later_1.3.1
[4] cellranger_1.1.0 polyclip_1.10-4 fastDummies_1.7.3
[7] lifecycle_1.0.3 rstatix_0.7.2 doParallel_1.0.17
[10] rprojroot_2.0.3 globals_0.16.2 processx_3.8.1
[13] lattice_0.21-8 hdf5r_1.3.8 MASS_7.3-60
[16] backports_1.4.1 magrittr_2.0.3 plotly_4.10.2
[19] sass_0.4.6 rmarkdown_2.22 jquerylib_0.1.4
[22] yaml_2.3.7 httpuv_1.6.11 sctransform_0.4.1
[25] spam_2.9-1 spatstat.sparse_3.0-1 reticulate_1.29
[28] cowplot_1.1.1 pbapply_1.7-0 abind_1.4-5
[31] Rtsne_0.16 BiocGenerics_0.48.1 git2r_0.32.0
[34] circlize_0.4.15 S4Vectors_0.40.2 IRanges_2.36.0
[37] irlba_2.3.5.1 listenv_0.9.0 spatstat.utils_3.0-3
[40] goftest_1.2-3 RSpectra_0.16-1 spatstat.random_3.1-5
[43] fitdistrplus_1.1-11 parallelly_1.36.0 leiden_0.4.3
[46] codetools_0.2-19 shape_1.4.6 tidyselect_1.2.0
[49] farver_2.1.1 stats4_4.3.0 matrixStats_1.0.0
[52] spatstat.explore_3.2-1 googledrive_2.1.0 jsonlite_1.8.5
[55] GetoptLong_1.0.5 ellipsis_0.3.2 progressr_0.13.0
[58] ggridges_0.5.4 survival_3.5-5 iterators_1.0.14
[61] foreach_1.5.2 tools_4.3.0 ica_1.0-3
[64] Rcpp_1.0.10 glue_1.6.2 gridExtra_2.3
[67] xfun_0.39 withr_2.5.0 fastmap_1.1.1
[70] fansi_1.0.4 callr_3.7.3 digest_0.6.31
[73] timechange_0.2.0 R6_2.5.1 mime_0.12
[76] colorspace_2.1-0 scattermore_1.2 tensor_1.5
[79] spatstat.data_3.0-1 utf8_1.2.3 generics_0.1.3
[82] data.table_1.14.8 httr_1.4.6 htmlwidgets_1.6.2
[85] whisker_0.4.1 uwot_0.1.14 pkgconfig_2.0.3
[88] gtable_0.3.3 lmtest_0.9-40 htmltools_0.5.5
[91] carData_3.0-5 dotCall64_1.0-2 clue_0.3-64
[94] scales_1.2.1 png_0.1-8 knitr_1.43
[97] rstudioapi_0.14 rjson_0.2.21 tzdb_0.4.0
[100] reshape2_1.4.4 curl_5.0.0 nlme_3.1-162
[103] GlobalOptions_0.1.2 cachem_1.0.8 zoo_1.8-12
[106] KernSmooth_2.23-21 parallel_4.3.0 miniUI_0.1.1.1
[109] pillar_1.9.0 vctrs_0.6.2 RANN_2.6.1
[112] promises_1.2.0.1 car_3.1-2 xtable_1.8-4
[115] cluster_2.1.4 evaluate_0.21 cli_3.6.1
[118] compiler_4.3.0 rlang_1.1.1 crayon_1.5.2
[121] future.apply_1.11.0 ggsignif_0.6.4 labeling_0.4.2
[124] ps_1.7.5 getPass_0.2-4 plyr_1.8.8
[127] fs_1.6.2 stringi_1.7.12 deldir_1.0-9
[130] assertthat_0.2.1 munsell_0.5.0 lazyeval_0.2.2
[133] spatstat.geom_3.2-1 Matrix_1.6-5 RcppHNSW_0.5.0
[136] hms_1.1.3 patchwork_1.1.2 bit64_4.0.5
[139] future_1.32.0 shiny_1.7.4 highr_0.10
[142] ROCR_1.0-11 gargle_1.4.0 igraph_1.4.3
[145] broom_1.0.4 bslib_0.4.2 bit_4.0.5